sample_correlated_gnp {igraph} | R Documentation |
Generate a new random graph from a given graph by randomly adding/removing edges
Description
Sample a new graph by perturbing the adjacency matrix of a given graph and shuffling its vertices.
Usage
sample_correlated_gnp(
old.graph,
corr,
p = edge_density(old.graph),
permutation = NULL
)
Arguments
old.graph |
The original graph. |
corr |
A scalar in the unit interval, the target Pearson correlation between the adjacency matrices of the original and the generated graph (the adjacency matrix being used as a vector). |
p |
A numeric scalar, the probability of an edge between two vertices, it must in the open (0,1) interval. The default is the empirical edge density of the graph. If you are resampling an Erdős-Rényi graph and you know the original edge probability of the Erdős-Rényi model, you should supply that explicitly. |
permutation |
A numeric vector, a permutation vector that is
applied on the vertices of the first graph, to get the second graph. If
|
Details
Please see the reference given below.
Value
An unweighted graph of the same size as old.graph
such
that the correlation coefficient between the entries of the two
adjacency matrices is corr
. Note each pair of corresponding
matrix entries is a pair of correlated Bernoulli random variables.
References
Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013). Seeded graph matching for correlated Erdős-Rényi graphs. https://arxiv.org/abs/1304.7844
See Also
Random graph models (games)
erdos.renyi.game()
,
sample_()
,
sample_bipartite()
,
sample_correlated_gnp_pair()
,
sample_degseq()
,
sample_dot_product()
,
sample_fitness()
,
sample_fitness_pl()
,
sample_forestfire()
,
sample_gnm()
,
sample_gnp()
,
sample_grg()
,
sample_growing()
,
sample_hierarchical_sbm()
,
sample_islands()
,
sample_k_regular()
,
sample_last_cit()
,
sample_pa()
,
sample_pa_age()
,
sample_pref()
,
sample_sbm()
,
sample_smallworld()
,
sample_traits_callaway()
,
sample_tree()
Examples
g <- sample_gnp(1000, .1)
g2 <- sample_correlated_gnp(g, corr = 0.5)
cor(as.vector(g[]), as.vector(g2[]))
g
g2